Overview

Dataset statistics

Number of variables10
Number of observations385500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.4 MiB
Average record size in memory80.0 B

Variable types

Numeric10

Alerts

area[2] is highly overall correlated with area[3] and 2 other fieldsHigh correlation
area[3] is highly overall correlated with area[2] and 3 other fieldsHigh correlation
negpmax[3] is highly overall correlated with area[2] and 3 other fieldsHigh correlation
negpmax[4] is highly overall correlated with pmax[4]High correlation
pmax[3] is highly overall correlated with area[2] and 3 other fieldsHigh correlation
pmax[4] is highly overall correlated with area[3] and 3 other fieldsHigh correlation
negpmax[3] is highly skewed (γ1 = -332.0010744)Skewed
negpmax[4] is highly skewed (γ1 = -298.1308851)Skewed
rms[2] has unique valuesUnique

Reproduction

Analysis started2024-01-24 23:03:33.887132
Analysis finished2024-01-24 23:03:52.452774
Duration18.57 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

area[2]
Real number (ℝ)

HIGH CORRELATION 

Distinct383324
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2880254
Minimum-1.7354297
Maximum136.87216
Zeros0
Zeros (%)0.0%
Negative95
Negative (%)< 0.1%
Memory size2.9 MiB
2024-01-25T00:03:52.533484image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-1.7354297
5-th percentile1.7997066
Q13.1164604
median4.9441736
Q37.8885341
95-th percentile15.357612
Maximum136.87216
Range138.60759
Interquartile range (IQR)4.7720737

Descriptive statistics

Standard deviation4.7581129
Coefficient of variation (CV)0.75669428
Kurtosis13.275473
Mean6.2880254
Median Absolute Deviation (MAD)2.1490341
Skewness2.5161136
Sum2424033.8
Variance22.639638
MonotonicityNot monotonic
2024-01-25T00:03:52.645852image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.357543945 4
 
< 0.1%
3.262741699 3
 
< 0.1%
3.363134766 3
 
< 0.1%
2.520401611 3
 
< 0.1%
3.142952881 3
 
< 0.1%
4.193493652 3
 
< 0.1%
2.669830322 3
 
< 0.1%
2.48253418 3
 
< 0.1%
4.707202148 3
 
< 0.1%
4.724487305 3
 
< 0.1%
Other values (383314) 385469
> 99.9%
ValueCountFrequency (%)
-1.735429687 1
< 0.1%
-1.166360474 1
< 0.1%
-1.026279297 1
< 0.1%
-0.8099230957 1
< 0.1%
-0.7416693115 1
< 0.1%
-0.7018518066 1
< 0.1%
-0.6931616211 1
< 0.1%
-0.682600708 1
< 0.1%
-0.5695043945 1
< 0.1%
-0.5102636719 1
< 0.1%
ValueCountFrequency (%)
136.8721558 1
< 0.1%
113.5135553 1
< 0.1%
108.3531738 1
< 0.1%
92.22590332 1
< 0.1%
87.85783997 1
< 0.1%
84.62052368 1
< 0.1%
82.46864441 1
< 0.1%
81.78101807 1
< 0.1%
80.82236328 1
< 0.1%
78.86475586 1
< 0.1%

tmax[2]
Real number (ℝ)

Distinct84531
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.221963
Minimum0
Maximum204.6
Zeros280
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-01-25T00:03:52.762569image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.4
Q171
median71.992175
Q386.4
95-th percentile181.6
Maximum204.6
Range204.6
Interquartile range (IQR)15.4

Descriptive statistics

Standard deviation43.514353
Coefficient of variation (CV)0.51666278
Kurtosis0.74791145
Mean84.221963
Median Absolute Deviation (MAD)1.1921754
Skewness0.99967732
Sum32467567
Variance1893.4989
MonotonicityNot monotonic
2024-01-25T00:03:52.970603image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.4 17826
 
4.6%
72.2 17682
 
4.6%
72.4 17053
 
4.4%
71.8 16803
 
4.4%
72 16431
 
4.3%
71.2 16428
 
4.3%
71 16026
 
4.2%
71.6 15769
 
4.1%
72.6 13206
 
3.4%
70.8 12088
 
3.1%
Other values (84521) 226188
58.7%
ValueCountFrequency (%)
0 280
0.1%
0.4 306
0.1%
0.6 227
0.1%
0.8 319
0.1%
1 310
0.1%
1.018730108 1
 
< 0.1%
1.048866421 1
 
< 0.1%
1.109954747 1
 
< 0.1%
1.116509284 1
 
< 0.1%
1.122875033 1
 
< 0.1%
ValueCountFrequency (%)
204.6 506
0.1%
204.4 141
 
< 0.1%
204.2 127
 
< 0.1%
204 86
 
< 0.1%
203.8 109
 
< 0.1%
203.6 52
 
< 0.1%
203.4 64
 
< 0.1%
203.2 37
 
< 0.1%
203 59
 
< 0.1%
202.9073846 1
 
< 0.1%

rms[2]
Real number (ℝ)

UNIQUE 

Distinct385500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3566277
Minimum0.25450023
Maximum5.1950231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-01-25T00:03:53.068087image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.25450023
5-th percentile0.83023245
Q11.1093896
median1.333165
Q31.5777393
95-th percentile1.9631291
Maximum5.1950231
Range4.9405229
Interquartile range (IQR)0.4683497

Descriptive statistics

Standard deviation0.3462679
Coefficient of variation (CV)0.25524165
Kurtosis0.39125227
Mean1.3566277
Median Absolute Deviation (MAD)0.23320375
Skewness0.42549044
Sum522979.99
Variance0.11990146
MonotonicityNot monotonic
2024-01-25T00:03:53.175095image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.456864989 1
 
< 0.1%
1.914290479 1
 
< 0.1%
1.092337413 1
 
< 0.1%
1.308422298 1
 
< 0.1%
1.340774334 1
 
< 0.1%
1.390414416 1
 
< 0.1%
1.076434846 1
 
< 0.1%
1.636784081 1
 
< 0.1%
0.8179451867 1
 
< 0.1%
1.053217029 1
 
< 0.1%
Other values (385490) 385490
> 99.9%
ValueCountFrequency (%)
0.2545002292 1
< 0.1%
0.2946413214 1
< 0.1%
0.3485190615 1
< 0.1%
0.3510521062 1
< 0.1%
0.3593454188 1
< 0.1%
0.3599342364 1
< 0.1%
0.3602115741 1
< 0.1%
0.3626820036 1
< 0.1%
0.3654531835 1
< 0.1%
0.3675273805 1
< 0.1%
ValueCountFrequency (%)
5.195023107 1
< 0.1%
4.886637881 1
< 0.1%
4.803247601 1
< 0.1%
4.777913244 1
< 0.1%
4.639697077 1
< 0.1%
4.594832418 1
< 0.1%
4.438121378 1
< 0.1%
4.337634438 1
< 0.1%
4.303626837 1
< 0.1%
4.209195796 1
< 0.1%

pmax[3]
Real number (ℝ)

HIGH CORRELATION 

Distinct381929
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.457888
Minimum2.0343109
Maximum136.65859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-01-25T00:03:53.276351image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum2.0343109
5-th percentile5.1153472
Q19.3273972
median15.071468
Q336.166834
95-th percentile84.475361
Maximum136.65859
Range134.62428
Interquartile range (IQR)26.839437

Descriptive statistics

Standard deviation25.397517
Coefficient of variation (CV)0.9599223
Kurtosis1.4993491
Mean26.457888
Median Absolute Deviation (MAD)7.6665704
Skewness1.5414483
Sum10199516
Variance645.03385
MonotonicityNot monotonic
2024-01-25T00:03:53.381458image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.30614929 4
 
< 0.1%
5.67930603 3
 
< 0.1%
5.602044678 3
 
< 0.1%
4.919451904 3
 
< 0.1%
14.51911316 3
 
< 0.1%
8.764260864 3
 
< 0.1%
18.50177612 3
 
< 0.1%
13.3940033 3
 
< 0.1%
11.38639526 3
 
< 0.1%
7.667312622 3
 
< 0.1%
Other values (381919) 385469
> 99.9%
ValueCountFrequency (%)
2.034310913 1
< 0.1%
2.162094116 1
< 0.1%
2.167385864 1
< 0.1%
2.209439087 1
< 0.1%
2.240426636 1
< 0.1%
2.254873657 1
< 0.1%
2.387930298 1
< 0.1%
2.389950562 1
< 0.1%
2.404840088 1
< 0.1%
2.41237793 1
< 0.1%
ValueCountFrequency (%)
136.6585876 1
< 0.1%
135.1032013 1
< 0.1%
134.6650848 1
< 0.1%
133.1647278 1
< 0.1%
132.5862 1
< 0.1%
131.7500427 1
< 0.1%
130.9431671 1
< 0.1%
130.9295593 1
< 0.1%
130.4724182 1
< 0.1%
129.8304504 1
< 0.1%

negpmax[3]
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct372049
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-13.964902
Minimum-25502.558
Maximum-1.1566278
Zeros0
Zeros (%)0.0%
Negative385500
Negative (%)100.0%
Memory size2.9 MiB
2024-01-25T00:03:53.484359image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-25502.558
5-th percentile-49.525641
Q1-17.757946
median-6.0601334
Q3-4.6805084
95-th percentile-3.6629848
Maximum-1.1566278
Range25501.401
Interquartile range (IQR)13.077438

Descriptive statistics

Standard deviation68.418678
Coefficient of variation (CV)-4.899331
Kurtosis116739.35
Mean-13.964902
Median Absolute Deviation (MAD)1.9813068
Skewness-332.00107
Sum-5383469.8
Variance4681.1155
MonotonicityNot monotonic
2024-01-25T00:03:53.585160image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.894543457 5
 
< 0.1%
-5.988519287 4
 
< 0.1%
-5.335021973 4
 
< 0.1%
-4.659609985 4
 
< 0.1%
-4.843499756 4
 
< 0.1%
-4.584515381 4
 
< 0.1%
-4.373394775 4
 
< 0.1%
-4.717922974 4
 
< 0.1%
-4.242272949 4
 
< 0.1%
-5.506130981 4
 
< 0.1%
Other values (372039) 385459
> 99.9%
ValueCountFrequency (%)
-25502.55777 1
< 0.1%
-23980.58646 1
< 0.1%
-22010.55508 1
< 0.1%
-2135.750092 1
< 0.1%
-1766.599709 1
< 0.1%
-1677.010896 1
< 0.1%
-541.1636109 1
< 0.1%
-451.3643812 1
< 0.1%
-188.425906 1
< 0.1%
-149.1700448 1
< 0.1%
ValueCountFrequency (%)
-1.156627817 1
< 0.1%
-1.298421492 1
< 0.1%
-1.370498657 1
< 0.1%
-1.375091039 1
< 0.1%
-1.474986483 1
< 0.1%
-1.513497201 1
< 0.1%
-1.632525635 1
< 0.1%
-1.633370154 1
< 0.1%
-1.689878248 1
< 0.1%
-1.700268555 1
< 0.1%

area[3]
Real number (ℝ)

HIGH CORRELATION 

Distinct384634
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.282057
Minimum-0.46547241
Maximum217.15825
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)< 0.1%
Memory size2.9 MiB
2024-01-25T00:03:53.684157image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-0.46547241
5-th percentile3.6415597
Q16.9004971
median10.510014
Q320.451969
95-th percentile42.20369
Maximum217.15825
Range217.62372
Interquartile range (IQR)13.551472

Descriptive statistics

Standard deviation12.07951
Coefficient of variation (CV)0.79043743
Kurtosis1.4187524
Mean15.282057
Median Absolute Deviation (MAD)4.6655106
Skewness1.401735
Sum5891233
Variance145.91456
MonotonicityNot monotonic
2024-01-25T00:03:53.783250image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.282794189 3
 
< 0.1%
4.262756348 3
 
< 0.1%
7.547821045 3
 
< 0.1%
19.79847168 2
 
< 0.1%
12.49109802 2
 
< 0.1%
23.36810059 2
 
< 0.1%
13.25037842 2
 
< 0.1%
10.06262207 2
 
< 0.1%
7.925891113 2
 
< 0.1%
6.3965271 2
 
< 0.1%
Other values (384624) 385477
> 99.9%
ValueCountFrequency (%)
-0.4654724121 1
< 0.1%
-0.3459466553 1
< 0.1%
-0.2434613037 1
< 0.1%
-0.09316650391 1
< 0.1%
-0.07318664551 1
< 0.1%
-0.06622741699 1
< 0.1%
-0.05329650879 1
< 0.1%
-0.02211914062 1
< 0.1%
-0.01951965332 1
< 0.1%
-0.007261352539 1
< 0.1%
ValueCountFrequency (%)
217.1582458 1
< 0.1%
153.298963 1
< 0.1%
121.8822687 1
< 0.1%
121.5279907 1
< 0.1%
109.2606598 1
< 0.1%
107.3340906 1
< 0.1%
103.3280176 1
< 0.1%
94.03706787 1
< 0.1%
93.56478638 1
< 0.1%
91.46968689 1
< 0.1%

tmax[3]
Real number (ℝ)

Distinct60405
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.975241
Minimum0
Maximum204.6
Zeros29
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-01-25T00:03:53.879797image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70.6
Q171.060475
median71.6
Q372.2
95-th percentile72.6
Maximum204.6
Range204.6
Interquartile range (IQR)1.1395249

Descriptive statistics

Standard deviation14.680792
Coefficient of variation (CV)0.20117498
Kurtosis39.83927
Mean72.975241
Median Absolute Deviation (MAD)0.6
Skewness5.0452427
Sum28131955
Variance215.52567
MonotonicityNot monotonic
2024-01-25T00:03:53.979836image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.4 32854
 
8.5%
71.8 32127
 
8.3%
71 31944
 
8.3%
72.2 31606
 
8.2%
72 31101
 
8.1%
71.2 30775
 
8.0%
71.6 30279
 
7.9%
70.8 26640
 
6.9%
72.4 25167
 
6.5%
70.6 16737
 
4.3%
Other values (60395) 96270
25.0%
ValueCountFrequency (%)
0 29
< 0.1%
0.4 30
< 0.1%
0.6 31
< 0.1%
0.8 23
< 0.1%
1 31
< 0.1%
1.17697823 1
 
< 0.1%
1.2 26
< 0.1%
1.211104404 1
 
< 0.1%
1.247763866 1
 
< 0.1%
1.287668663 1
 
< 0.1%
ValueCountFrequency (%)
204.6 39
< 0.1%
204.4 10
 
< 0.1%
204.2 4
 
< 0.1%
204 9
 
< 0.1%
203.8 3
 
< 0.1%
203.6 11
 
< 0.1%
203.4 3
 
< 0.1%
203.2 1
 
< 0.1%
203 6
 
< 0.1%
202.8 10
 
< 0.1%

rms[3]
Real number (ℝ)

Distinct385498
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3256906
Minimum0.28391407
Maximum5.5505564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-01-25T00:03:54.079532image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.28391407
5-th percentile0.81225352
Q11.084392
median1.3015152
Q31.5416934
95-th percentile1.9197395
Maximum5.5505564
Range5.2666423
Interquartile range (IQR)0.45730144

Descriptive statistics

Standard deviation0.33835609
Coefficient of variation (CV)0.25523007
Kurtosis0.4379662
Mean1.3256906
Median Absolute Deviation (MAD)0.22766373
Skewness0.4340884
Sum511053.71
Variance0.11448484
MonotonicityNot monotonic
2024-01-25T00:03:54.177551image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.454878934 2
 
< 0.1%
1.445435086 2
 
< 0.1%
1.531228158 1
 
< 0.1%
1.453444733 1
 
< 0.1%
0.7081381119 1
 
< 0.1%
0.9451061799 1
 
< 0.1%
1.337340382 1
 
< 0.1%
1.028406702 1
 
< 0.1%
1.21665139 1
 
< 0.1%
1.87870439 1
 
< 0.1%
Other values (385488) 385488
> 99.9%
ValueCountFrequency (%)
0.2839140674 1
< 0.1%
0.2978318788 1
< 0.1%
0.3042772149 1
< 0.1%
0.3100630966 1
< 0.1%
0.314337983 1
< 0.1%
0.3182819812 1
< 0.1%
0.3244101072 1
< 0.1%
0.3263722937 1
< 0.1%
0.3281416113 1
< 0.1%
0.3314276824 1
< 0.1%
ValueCountFrequency (%)
5.550556353 1
< 0.1%
5.125494881 1
< 0.1%
4.85576616 1
< 0.1%
4.83616478 1
< 0.1%
4.621285432 1
< 0.1%
4.571653835 1
< 0.1%
4.397452441 1
< 0.1%
4.374339071 1
< 0.1%
4.218837331 1
< 0.1%
4.210896419 1
< 0.1%

pmax[4]
Real number (ℝ)

HIGH CORRELATION 

Distinct379009
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.947826
Minimum1.940271
Maximum90.635233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 MiB
2024-01-25T00:03:54.275980image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum1.940271
5-th percentile4.1610776
Q15.7443343
median10.180335
Q317.985703
95-th percentile46.298861
Maximum90.635233
Range88.694962
Interquartile range (IQR)12.241369

Descriptive statistics

Standard deviation13.394484
Coefficient of variation (CV)0.89608245
Kurtosis3.6467794
Mean14.947826
Median Absolute Deviation (MAD)4.9657176
Skewness1.9387501
Sum5762386.9
Variance179.41221
MonotonicityNot monotonic
2024-01-25T00:03:54.371356image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.057263184 4
 
< 0.1%
4.668814087 4
 
< 0.1%
5.003460693 4
 
< 0.1%
4.668426514 4
 
< 0.1%
5.831195068 4
 
< 0.1%
8.505041504 3
 
< 0.1%
6.129846191 3
 
< 0.1%
4.603619385 3
 
< 0.1%
4.68112793 3
 
< 0.1%
7.323895264 3
 
< 0.1%
Other values (378999) 385465
> 99.9%
ValueCountFrequency (%)
1.940270996 1
< 0.1%
1.994177246 1
< 0.1%
2.018829346 1
< 0.1%
2.033197021 1
< 0.1%
2.045141602 1
< 0.1%
2.122415161 1
< 0.1%
2.141015625 1
< 0.1%
2.159579468 1
< 0.1%
2.18973999 1
< 0.1%
2.196051025 1
< 0.1%
ValueCountFrequency (%)
90.63523254 1
< 0.1%
90.28744202 1
< 0.1%
89.36125488 1
< 0.1%
87.50843506 1
< 0.1%
87.27154541 1
< 0.1%
86.33645935 1
< 0.1%
86.2472229 1
< 0.1%
86.09331665 1
< 0.1%
85.68477783 1
< 0.1%
84.9951355 1
< 0.1%

negpmax[4]
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct363861
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.4542001
Minimum-42648.221
Maximum-0.87650499
Zeros0
Zeros (%)0.0%
Negative385500
Negative (%)100.0%
Memory size2.9 MiB
2024-01-25T00:03:54.468446image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-42648.221
5-th percentile-23.988506
Q1-7.3876602
median-5.3240097
Q3-4.5065163
95-th percentile-3.6246515
Maximum-0.87650499
Range42647.345
Interquartile range (IQR)2.8811439

Descriptive statistics

Standard deviation115.44
Coefficient of variation (CV)-13.654751
Kurtosis95390.224
Mean-8.4542001
Median Absolute Deviation (MAD)1.0418579
Skewness-298.13089
Sum-3259094.1
Variance13326.393
MonotonicityNot monotonic
2024-01-25T00:03:54.653618image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.908303833 5
 
< 0.1%
-4.35189209 5
 
< 0.1%
-5.315161133 5
 
< 0.1%
-4.585043335 5
 
< 0.1%
-4.676217651 4
 
< 0.1%
-4.908898926 4
 
< 0.1%
-4.242736816 4
 
< 0.1%
-4.116256714 4
 
< 0.1%
-3.87611084 4
 
< 0.1%
-4.50796814 4
 
< 0.1%
Other values (363851) 385456
> 99.9%
ValueCountFrequency (%)
-42648.22138 1
< 0.1%
-38012.33874 1
< 0.1%
-29361.27607 1
< 0.1%
-22547.07721 1
< 0.1%
-19198.84605 1
< 0.1%
-5810.813366 1
< 0.1%
-5763.318605 1
< 0.1%
-4514.03277 1
< 0.1%
-3704.414228 1
< 0.1%
-3695.546609 1
< 0.1%
ValueCountFrequency (%)
-0.8765049893 1
< 0.1%
-0.9554986142 1
< 0.1%
-1.279119885 1
< 0.1%
-1.312775944 1
< 0.1%
-1.351813825 1
< 0.1%
-1.382180038 1
< 0.1%
-1.383336623 1
< 0.1%
-1.401692709 1
< 0.1%
-1.403765327 1
< 0.1%
-1.481176776 1
< 0.1%

Interactions

2024-01-25T00:03:50.627805image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:40.658427image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:41.815473image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:43.008380image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:44.054897image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:45.136848image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:46.207542image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:47.256772image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:48.413989image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:49.519907image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:50.745811image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:40.781008image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:41.927709image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:43.120128image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:44.168257image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:45.248142image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:46.323147image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:47.365650image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:48.528919image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:49.633126image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:50.854575image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:40.893843image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:42.031738image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:43.218174image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:44.272173image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:45.350180image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:46.425864image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:47.467823image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:48.639874image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:49.743049image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:50.963489image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:41.007922image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:42.144952image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:43.323282image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:44.378134image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:45.452185image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:46.530262image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:47.568852image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:48.751411image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:49.854275image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:51.078053image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:41.127918image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:42.262009image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:43.435599image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:44.488835image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:45.561800image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:46.643114image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:47.676360image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:48.869745image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:49.968445image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:51.186693image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:41.242086image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:42.380067image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:43.541028image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:44.604132image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:45.665924image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:46.748119image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:47.785048image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:48.980302image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:50.082241image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:51.293323image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:41.358453image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:42.489738image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:43.641133image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:44.711088image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:45.776298image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:46.847956image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:47.979869image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:49.088481image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:50.192051image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:51.401448image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:41.472165image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:42.598104image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:43.748852image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:44.819809image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:45.877742image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:46.949867image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:48.085047image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:49.193403image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:50.302419image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:51.508741image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:41.584553image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:42.713757image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:43.851149image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:44.926330image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:45.983423image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:47.050932image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:48.195507image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:49.301445image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:50.411120image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:51.614196image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:41.696254image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:42.903919image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:43.953013image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:45.031880image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:46.087836image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:47.154736image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:48.301200image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:49.409951image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-25T00:03:50.521140image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Correlations

2024-01-25T00:03:54.726079image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
area[2]area[3]negpmax[3]negpmax[4]pmax[3]pmax[4]rms[2]rms[3]tmax[2]tmax[3]
area[2]1.0000.535-0.511-0.0950.5460.264-0.003-0.002-0.088-0.112
area[3]0.5351.000-0.740-0.3300.9400.603-0.002-0.002-0.138-0.199
negpmax[3]-0.511-0.7401.0000.351-0.817-0.5410.002-0.0020.1400.198
negpmax[4]-0.095-0.3300.3511.000-0.371-0.5710.0000.0020.0500.084
pmax[3]0.5460.940-0.817-0.3711.0000.673-0.002-0.001-0.144-0.213
pmax[4]0.2640.603-0.541-0.5710.6731.000-0.000-0.001-0.078-0.134
rms[2]-0.003-0.0020.0020.000-0.002-0.0001.0000.005-0.0330.000
rms[3]-0.002-0.002-0.0020.002-0.001-0.0010.0051.000-0.000-0.006
tmax[2]-0.088-0.1380.1400.050-0.144-0.078-0.033-0.0001.0000.295
tmax[3]-0.112-0.1990.1980.084-0.213-0.1340.000-0.0060.2951.000

Missing values

2024-01-25T00:03:51.716956image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-25T00:03:51.942908image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

area[2]tmax[2]rms[2]pmax[3]negpmax[3]area[3]tmax[3]rms[3]pmax[4]negpmax[4]
03.13232872.2000001.4568653.811328-23.7472933.62197315.2000001.5312284.893027-18.008972
13.26746155.0000001.7090023.940369-5.4270023.444678200.2000001.1724975.549379-4.670676
22.92915028.0000001.3733364.903412-4.5592351.223700204.6000001.5086283.795407-4.761539
31.54437014.0000001.1150783.791672-4.9812162.985681104.0000001.3741084.848719-3.985501
44.21104771.2000001.2372685.123048-4.4471044.37770871.3572371.4175053.026709-6.137964
52.624590135.8000001.4934975.695571-4.4691386.9829114.4060391.6964553.862329-5.890676
64.050229181.0000001.1265904.469376-4.3652192.882394178.0000001.6926084.227777-5.080002
75.09346480.4889070.9567075.109399-4.9646362.771094141.0000001.8182423.727237-6.876218
82.61165571.8000001.3522664.416956-4.8602662.489751131.8000001.1431943.877835-5.093504
92.14744911.0000001.6603394.369622-3.9507903.44722943.7799211.9457525.721975-3.400754
area[2]tmax[2]rms[2]pmax[3]negpmax[3]area[3]tmax[3]rms[3]pmax[4]negpmax[4]
38549025.78188272.21.50236566.807010-32.51644629.93843172.21.55094510.707587-5.343597
38549133.37784971.61.07295272.582571-37.02991634.85577171.61.34490914.167755-5.521088
38549231.47920871.20.94034972.944870-36.65208435.19083371.20.99026514.587003-3.457065
38549333.48727471.01.08151167.389642-37.94595933.55770971.02.15287713.273743-5.638428
38549431.56450672.21.39198870.385419-38.83559035.56930272.02.03656412.418210-5.175967
38549528.62158271.00.78251263.119604-36.91085232.03450471.01.82978511.942999-3.202997
38549628.43558271.41.39013959.971158-37.72125532.35001571.41.31645811.555255-5.173907
38549730.50035171.21.05646473.850089-38.34437033.45696171.21.87405716.884897-5.173453
38549827.11007671.21.19871259.277927-36.10339131.00820171.22.13999712.191660-4.629693
38549927.38700071.61.26476864.351324-37.44896930.59162071.61.14619214.529691-17.717380